The L2 norm, also known as the Euclidean norm, measures the distance of a vector from the origin in a multi-dimensional space. It is calculated by taking the square root of the sum of the squares of its components. For example, for a vector \mathbfx = (x_1, x_2, \ldots, x_n) , the L2 norm is given by ||\mathbfx||_2 = \sqrtx_1^2 + x_2^2 + \ldots + x_n^2 . The L2 norm is widely used in various fields, including machine learning and data analysis, to quantify the size or length of a vector. It helps in optimizing algorithms, particularly in gradient descent methods, where minimizing the L2 norm can lead to better model performance and accuracy.